Transformative Potential Research Articles

Lensless fiber endomicroscopy in biomedicine

Lensless fiber endomicroscopy, an emergent paradigm shift for minimally-invasive microscopic optical imaging and targeted light delivery, holds transformative potential, especially in biomedicine. Leveraging holographic detection and physical or computational wavefront correction, it enables three-dimensional imaging in an unprecedentedly small footprint, which is crucial for various applications such as brain surgery. This perspective reviews the recent breakthroughs, highlighting potential emerging applications, and pinpointing gaps between innovation and real-world applications. As the research in this realm accelerates, the novel breakthroughs and existing frontiers highlighted in this perspective can be used as guidelines for researchers joining this exciting domain.

Experimental Study on Automated Controller of Street Light Management System using IoT

In order to ensure public safety and support nighttime activities, street lighting is crucial to the urban landscape, and this study starts by contextualizing its importance. It then goes on to analyze the shortcomings of the current street lighting systems, which are typified by their static control methods and poor environmental response.These shortcomings underscore the urgent need for a paradigm shift towards more intelligent and adaptive solutions. The core proposition of this project lies in the design and implementation of an advanced street light management system empowered by IoT technology. At its heart, the system comprises a sophisticated network of interconnected devices, including sensors, actuators, and a central control unit. These components collaborate synergistically to capture, analyze, and act upon real-time data gleaned from the surrounding environment. Through the application of cutting-edge data analytics and machine learning algorithms, the system autonomously adjusts lighting levels in accordance with fluctuating parameters such as ambient light levels, traffic density, weather conditions, and pedestrian activity. By dynamically optimizing energy usage while ensuring adequate illumination, the system not only enhances operational efficiency but also contributes to cost reduction and environmental sustainability. Moreover, the proposed system incorporates proactive maintenance features, enabling early fault detection and remote diagnostics. This capability minimizes downtime and enhances system reliability, thereby mitigating the operational challenges associated with traditional street lighting infrastructure. The project further explores the conceptual framework of IoT, elucidating its transformative potential in revolutionizing urban infrastructure management. By seamlessly integrating physical devices with digital intelligence, IoT facilitates the creation of interconnected ecosystems wherein data-driven decision-making and automation converge to drive unprecedented efficiency and innovation

Linking farmers’ perceptions and management decision toward sustainable agroecological transition: evidence from rural Tunisia

Global food systems face sustainability challenges like undernourishment, inequity, resource degradation, and pollution. Food production and consumption drive environmental change with greenhouse gas emissions, biodiversity loss, and land-system shifts. The climate change crisis has intensified concerns about the ecological impact of these systems. Sustainable food networks, such as community-supported agriculture, are promoting sustainable production and consumption through short supply chains. International bodies like the Food and Agriculture Organization (FAO) and the Consultative Group for International Agricultural Research (CGIAR) are also spearheading initiatives for more equitable and sustainable food systems. In Tunisia, where dryland areas predominate, the ongoing implementation of the Agroecology Initiative provides the context for this study, which explores the drivers and barriers of agroecological transformation in this challenging environment. The research focuses on stakeholder engagement, with a gender perspective to explore farmer perceptions. The study, conducted in the northwest of Tunisia in 2022–2023, involved focus groups, workshops, surveys, and questionnaires with various stakeholders. Findings highlight farmer organizations’ potential in promoting sustainable farming, with clear goals, diversified systems, and collaborations. However, challenges such as input scarcity, water shortage, low income, and marketing must be addressed. Results also indicate that over 90% of farmers who received assistance with agroecological practices reported a change in their ideas and practices. Fifty seven percent of the workshops participants identified the olive oil value chain as having the greatest potential for agroecological transformation, but it faces constraints such as climate, lack of policy incentives, training, funding, and difficulty in adopting technical innovations. Women’s inclusion in agriculture, environmental, social, and economic challenges were also highlighted. Despite these obstacles, key drivers for agroecological transition were identified. These include the compatibility of many agroecological practices with existing farmer capabilities, their cultural and economic benefits, and the positive outcomes for environmental sustainability and health. The study advocates for a socio-technical systems analysis to address the root causes hindering Tunisia’s agroecological transformation. A participatory approach is crucial to understanding priorities and developing a sustainable and resilient food system. Furthermore, the research underscores the importance of considering diverse farmer perspectives and tailoring strategies to support this critical transition effectively.

Monetary Policy, Digitalisation and Banking Industry Performance in Nigeria

In the evolving financial landscape of Nigeria, the interplay between monetary policy and digitalization significantly influences the performance metrics of banks. This study explores the multifaceted impact of these two critical factors on the Nigerian banking sector. Monetary policy, orchestrated by the Central Bank of Nigeria, regulates the economy's money supply, affecting banks' lending behaviors and liquidity. Digitalization, on the other hand, offers a transformative potential for banks to enhance operational efficiency, customer experience, and service delivery through technological advancements. The study examines how money supply, lending rate and digitalization (proxied by mobile cell subscription) affect banks’ financial performance, focusing on bank credit and liquid assets, while controlling for inflation and exchange rate variables. It also examines the interaction between digitalization and monetary policy variables on bank performance. Employing an interactive multiple regression model, the study analyzes time series data from 1996 to 2022 sourced from the World Bank. Findings indicate that while stringent monetary policies can constrain bank performance by tightening credit and reducing liquidity, digitalization provides an opportunity for banks to counteract these effects through enhanced efficiency. However, the successful integration of digital technologies is contingent upon adequate investment, regulatory support, and strategic alignment with the banks’ core objectives. The study concludes that Nigerian banks must adeptly manage monetary policy fluctuations and leverage digitalization to sustain and enhance performance. These insights could guide policy formulation and strategic decision-making within the banking industry.

Dynamic mirroring: unveiling the role of digital twins, artificial intelligence and synthetic data for personalized medicine in laboratory medicine.

In recent years, the integration of technological advancements and digitalization into healthcare has brought about a remarkable transformation in care delivery and patient management. Among these advancements, the concept of digital twins (DTs) has recently gained attention as a tool with substantial transformative potential in different clinical contexts. DTs are virtual representations of a physical entity (e.g., a patient or an organ) or systems (e.g., hospital wards, including laboratories), continuously updated with real-time data to mirror its real-world counterpart. DTs can be utilized to monitor and customize health care by simulating an individual's health status based on information from wearables, medical devices, diagnostic tests, and electronic health records. In addition, DTs can be used to define personalized treatment plans. In this study, we focused on some possible applications of DTs in laboratory medicine when used with AI and synthetic data obtained by generative AI. The first point discussed how biological variation (BV) application could be tailored to individuals, considering population-derived BV data on laboratory parameters and circadian or ultradian variations. Another application could be enhancing the interpretation of tumor markers in advanced cancer therapy and treatments. Furthermore, DTs applications might derive personalized reference intervals, also considering BV data or they can be used to improve test results interpretation. DT's widespread adoption in healthcare is not imminent, but it is not far off. This technology will likely offer innovative and definitive solutions for dynamically evaluating treatments and more precise diagnoses for personalized medicine.

MACHINE LEARNING AND THE STUDY OF LANGUAGE CHANGE: A REVIEW OF METHODOLOGIES AND APPLICATION

Language change, a fundamental aspect of human communication, has long been a central focus in linguistic research. Traditional methods of analysis, while valuable, have been limited by the scale and complexity of linguistic data. The advent of machine learning (ML) offers transformative potential in this field, enabling the analysis of vast datasets and the discovery of subtle patterns that may elude manual scrutiny. This review paper comprehensively examines the current state of ML methodologies in the study of language change, synthesizing findings from 67 peer-reviewed articles. We delve into diverse ML approaches, including supervised, unsupervised, and deep learning techniques, and critically evaluate their applications across various linguistic domains, such as historical linguistics, sociolinguistics, and language contact. We address challenges related to data availability, bias, and model interpretability, emphasizing the need for transparent and rigorous methodologies. By summarizing key findings and outlining future directions, this review aims to foster interdisciplinary collaboration between linguists and computer scientists, advancing our understanding of the complex dynamics of language evolution.

Nanotechnology's potential in advancing renewable energy solutions

Nanotechnology has emerged as a promising frontier in the quest for sustainable energy solutions, offering transformative opportunities to address pressing challenges in renewable energy generation, storage, and conversion. This review explores the potential of nanotechnology in advancing renewable energy solutions, encompassing a wide range of applications spanning solar energy, wind energy, energy storage, and fuel cells. By leveraging the unique properties of nanomaterials, such as high surface area-to-volume ratio, tunable optical and electronic properties, and enhanced catalytic activity, researchers and engineers can develop innovative materials and devices with unprecedented performance and efficiency. Nanotechnology-enabled advancements in solar photovoltaics include the development of next-generation solar cells incorporating nanostructured materials, such as quantum dots, nanowires, and perovskite-based solar cells, to enhance light absorption, charge transport, and overall power conversion efficiency. In the realm of energy storage, nanomaterials hold promise for improving the performance and longevity of batteries, supercapacitors, and other energy storage devices through enhanced electrode materials, electrolytes, and nanostructured architectures. Furthermore, nanotechnology-driven innovations in wind energy, such as lightweight and durable nanocomposite materials for turbine blades, and advancements in fuel cell technologies, including catalyst nanoparticles for efficient hydrogen production and conversion, exemplify the diverse applications and transformative potential of nanotechnology in renewable energy. Through a comprehensive analysis of recent research and development efforts, this abstract underscores the critical role of nanotechnology in accelerating the transition to a sustainable and renewable energy future. Keywords: Nanotechnology, Renewable Energy, Solar Energy, Wind Energy, Energy Storage, Nanomaterials.

Investigating the Economic Dynamics of Mobile Money in Post-Conflict Economies: Empirical Evidence from Somalia

This study investigates the economic dynamics of mobile money in Somalia, focusing on its transformative potential to impact economic activities in the short and long term. Adopting a quantitative research design, the study utilizes secondary data from 2010 to 2022. The autoregressive distributed lag (ARDL) approach is employed to assess the impact of mobile money on household consumption, real income, exports, and imports. Theoretical insights from the quantity theory of money further guide the examination of how mobile money influences economic activities. The findings demonstrate that mobile money has a significant positive effect on household consumption, real income, export growth, and import activity both in the short and long term. The study reveals that mobile money not only facilitates financial transactions but also enhances trade efficiency and promotes financial inclusion, thereby stimulating economic activities and growth. The study underscores the need for a supportive regulatory environment for mobile money services. It highlights that financial institutions and mobile money service providers should expand innovative services to include the unbanked population. By doing so, they can leverage mobile money to foster greater economic engagement and empowerment. Policymakers are encouraged to develop strategies that promote financial inclusion and economic development to harness the full potential of mobile money. This study enriches the existing literature by uniquely investigating the economic impacts of mobile money in Somalia, a post-conflict setting, thereby broadening the understanding of how financial technologies can catalyze economic activities and growth in similar contexts.

FOOD CLASSIFICATION AND CALORIE ESTIMATION USING CNN

This research paper explores the transformative impact of deep learning methodologies in the domains of food image recognition, classification, and calorie estimation. By utilizing the convergence of machine learning with convolutional neural networks (CNNs), the study tackles the growing concerns surrounding obesity and health difficulties, highlighting the need for creative methods of nutritional monitoring. Motivated by challenges in fruit classification and the limitations of traditional methods, the research progressively shifts towards efficient and accurate image-based approaches, particularly highlighting the effectiveness of CNNs. The methodology focuses on machine learning-based approaches for food calorie estimation, with a specific emphasis on parameter-optimized lightweight CNN models utilizing TensorFlow's Object Detection API and CNN. The results showcase impressive accuracy levels, often exceeding 90%, across diverse food items, albeit with persistent challenges such as limited datasets, computational costs, and real-world application constraints. In conclusion, this comprehensive review underscores the transformative potential of deep learning in fostering healthier lifestyles, combating obesity, and providing valuable insights for dietary guidance and health management. Further research is recommended to enhance dataset representativeness and optimize model generalization for broader practical applicability Keywords: Calorie estimation, Classification, Convolutional neural network, Object detection.

SURVEY ON FOOD CLASSIFICATION AND CALORIE ESTIMATION USING CNN

This research paper explores the transformative impact of deep learning methodologies in the domains of food image recognition, classification, and calorie estimation. By utilizing the convergence of machine learning with convolutional neural networks (CNNs), the study tackles the growing concerns surrounding obesity and health difficulties, highlighting the need for creative methods of nutritional monitoring. Motivated by challenges in fruit classification and the limitations of traditional methods, the research progressively shifts towards efficient and accurate image-based approaches, particularly highlighting the effectiveness of CNNs. The methodology focuses on machine learning-based approaches for food calorie estimation, with a specific emphasis on parameter-optimized lightweight CNN models utilizing TensorFlow's Object Detection API and Mask R-CNN. The results showcase impressive accuracy levels, often exceeding 90%, across diverse food items, albeit with persistent challenges such as limited datasets, computational costs, and real-world application constraints. In conclusion, this comprehensive review underscores the transformative potential of deep learning in fostering healthier lifestyles, combating obesity, and providing valuable insights for dietary guidance and health management. Further research is recommended to enhance dataset representativeness and optimize model generalization for broader practical applicability Keywords: Calorie estimation, Classification, Convolutional neural network, Object detection.

The Role of Education in Promoting Empowerment as Agency in the Lives of Women and Girls in Somaliland

This article draws on 100 qualitative interviews with young women who participated in a girls’ education programme known as SOMGEP (Somali Girls Education Programme). The underpinning research sought to explore the impact of SOMGEP education on and for the later life outcomes of the participating girls now women. Researchers were able to revisit former SOMGEP students 7 years after the end of the programme. The findings made links between higher levels of educational attendance and attainment and its positive impact on agency. What also emerged was the ways in which the expression of agency is hampered by wider contextual factors that often act as a backlash to the transformative potential of a secular education.

Harnessing artificial intelligence to develop strategic marketing goals

In today's rapidly evolving business landscape, harnessing artificial intelligence (AI) has become imperative for organizations striving to remain competitive. This concept paper explores the integration of AI in developing strategic marketing goals, aiming to provide a comprehensive framework for leveraging AI's capabilities effectively. The paper begins by outlining the current state of AI in marketing, highlighting its transformative potential across various facets such as customer segmentation, personalized targeting, predictive analytics, and automation. It underscores the necessity for businesses to embrace AI-driven approaches to stay ahead in an increasingly data-driven marketplace. Moreover, the concept paper delves into the strategic implications of AI adoption in marketing. It emphasizes the need for alignment between AI initiatives and overarching business objectives, emphasizing the role of AI in enhancing customer experience, optimizing resource allocation, and driving revenue growth. Furthermore, the paper addresses key considerations and challenges associated with integrating AI into marketing strategies, including data privacy concerns, ethical implications, and organizational readiness. It advocates for a structured approach to AI implementation, encompassing robust data governance, talent acquisition, and continuous monitoring to mitigate risks effectively. Drawing upon industry best practices and case studies, the concept paper elucidates practical methodologies for leveraging AI in setting strategic marketing goals. It emphasizes the importance of data-driven decision-making, iterative experimentation, and agile adaptation to capitalize on AI's potential for driving innovation and competitive advantage. In conclusion, the concept paper advocates for a proactive stance towards embracing AI in marketing strategy formulation. By strategically harnessing AI's capabilities, organizations can not only enhance their marketing effectiveness but also cultivate long-term sustainable growth in today's dynamic business environment. Keywords: AI, Strategic, Marketing, Environment, Goals, Business.

Enhancing cervical cancer detection and robust classification through a fusion of deep learning models

Cervical cancer, the second most prevalent cancer affecting women, arises from abnormal cell growth in the cervix, a crucial anatomical structure within the uterus. The significance of early detection cannot be overstated, prompting the use of various screening methods such as Pap smears, colposcopy, and Human Papillomavirus (HPV) testing to identify potential risks and initiate timely intervention. These screening procedures encompass visual inspections, Pap smears, colposcopies, biopsies, and HPV-DNA testing, each demanding the specialized knowledge and skills of experienced physicians and pathologists due to the inherently subjective nature of cancer diagnosis. In response to the imperative for efficient and intelligent screening, this article introduces a groundbreaking methodology that leverages pre-trained deep neural network models, including Alexnet, Resnet-101, Resnet-152, and InceptionV3, for feature extraction. The fine-tuning of these models is accompanied by the integration of diverse machine learning algorithms, with ResNet152 showcasing exceptional performance, achieving an impressive accuracy rate of 98.08%. It is noteworthy that the SIPaKMeD dataset, publicly accessible and utilized in this study, contributes to the transparency and reproducibility of our findings. The proposed hybrid methodology combines aspects of DL and ML for cervical cancer classification. Most intricate and complicated features from images can be extracted through DL. Further various ML algorithms can be implemented on extracted features. This innovative approach not only holds promise for significantly improving cervical cancer detection but also underscores the transformative potential of intelligent automation within the realm of medical diagnostics, paving the way for more accurate and timely interventions.

IoT Solutions with Artificial Intelligence Technologies for Precision Agriculture: Definitions, Applications, Challenges, and Opportunities

The global agricultural sector confronts significant obstacles such as population growth, climate change, and natural disasters, which negatively impact food production and pose a threat to food security. In response to these challenges, the integration of IoT and AI technologies emerges as a promising solution, facilitating data-driven decision-making, optimizing resource allocation, and enhancing monitoring and control systems in agricultural operations to address these challenges and promote sustainable farming practices. This study examines the intersection of IoT and AI in precision agriculture (PA), aiming to provide a comprehensive understanding of their combined impact and mutually reinforcing relationship. Employing a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, we explore the synergies and transformative potential of integrating IoT and AI in agricultural systems. The review also aims to identify present trends, challenges, and opportunities in utilizing IoT and AI in agricultural systems. Diverse forms of agricultural practices are scrutinized to discern the applications of IoT and AI systems. Through a critical analysis of existing literature, this study contributes to a deeper understanding of how the integration of IoT and AI technologies can revolutionize PA, resulting in improved efficiency, sustainability, and productivity in the agricultural sector.

The Impact of Artificial Intelligence on Legal Systems: Challenges and Opportunities

The integration of artificial intelligence into legal systems has engendered a paradigm shift in the legal landscape, presenting a complex interplay of challenges and opportunities for the legal profession and the justice system. This Comprehensive research delves into the multifaceted impact of artificial intelligence on legal systems, focusing on its transformative potential and implications. Through an extensive analysis of the integration of artificial intelligence technologies, including natural language processing, machine learning, and predictive analytics, the study illuminates the profound improvements in legal research, decision-making processes, and case management, emphasizing the unprecedented efficiency and accessibility that artificial intelligence offers within the legal domain. Furthermore, the research critically examines the ethical and societal challenges stemming from artificial intelligence integration, including concerns related to data privacy, algorithmic bias, and the accountability of artificial intelligence-driven legal solutions. By scrutinizing the existing regulatory frameworks governing artificial intelligence implementation, the study underscores the necessity of responsible and ethical artificial intelligence integration, advocating for transparency, fairness, and equitable practices in the legal profession. The findings contribute to the ongoing discourse on the ethical implications and effective management of artificial intelligence integration in legal systems, providing valuable insights and recommendations for stakeholders and policymakers to navigate the complexities and ensure the responsible adoption of artificial intelligence technologies within the legal sphere

Unlocking Tomorrow’s Landscape: How AI Will Reshape Customer Journeys (B2B & B2C)

Businesses must ask themselves this question as we approach a revolutionary era: how will artificial intelligence (AI) change the B2B and B2C customer journey landscapes? This capstone study explores and covers the journey of artificial intelligence (AI), it’s impact on the business, the new opportunities along with challenges and the future predictions following the literature reviews, case studies, and expert insights. The research unveils a wide variety of possibilities waiting to be unlocked. This research focuses on the transformative potential of artificial intelligence (AI) for businesses and customers alike. It serves as a valuable resource for business leaders, marketing and sales professionals, customer service teams to managing artificial intelligence (AI) adoption plans and optimizing customer journey opportunities. Using AI-powered tools for personalized engagement and data-driven decisions. Using AI to improve productivity and customize assistance. Includes key Findings like Personalized Experiences, Data-Driven Decisions Automation & Efficiency. AI-powered chatbots offer seamless B2B customer service, while B2C brands use recommendation engines to provide hyper-personalized product suggestions. AI streamlines B2B workflows, boosting productivity. Beyond these, the research identifies: Opportunities: How AI increases customer engagement to a greater extent, unlocking brand loyalty and advocacy. Areas to integration of AI in business regular process. Challenges: What could be the Ethical considerations and potential job displacement carefully.

Lightweight Convolutional Network with Integrated Attention Mechanism for Missing Bolt Detection in Railways

Railway infrastructure safety is a paramount concern, with bolt integrity being a critical component. In the realm of railway maintenance, the detection of missing bolts is a vital task that ensures the stability and safety of tracks. Traditionally, this task has been approached through manual inspections or conventional automated methods, which are often time-consuming, costly, and prone to human error. Addressing these challenges, this paper presents a state-of-the-art solution with the development of a lightweight convolutional neural network (CNN) featuring an integrated attention mechanism. This novel model is engineered to be computationally efficient while maintaining high accuracy, making it particularly suitable for real-time analysis in resource-constrained environments commonly found in railway inspections. The proposed CNN utilises a distinctive architecture that synergises the speed of lightweight networks with the precision of attention-based mechanisms. By integrating an attention mechanism, the network selectively concentrates on regions of interest within the image, effectively enhancing the model’s capability to identify missing bolts with remarkable accuracy. Comprehensive testing showcases a remarkable 96.43% accuracy and an impressive 96 F1-score, substantially outperforming existing deep learning frameworks in the context of missing bolt detection. Key contributions of this research include the model’s innovative attention-integrated approach, which significantly reduces the model complexity without compromising detection performance. Additionally, the model offers scalability and adaptability to various railway settings, proving its efficacy not just in controlled environments but also in diverse real-world scenarios. Extensive experiments, rigorous evaluations, and real-time deployment results collectively underscore the transformative potential of the presented CNN model in advancing the domain of railway safety maintenance.

The ‘Implicit Intelligence’ of artificial intelligence. Investigating the potential of large language models in social science research

ABSTRACT Researchers in ‘hard' science disciplines are exploring the transformative potential of Artificial Intelligence (AI) for advancing research in their fields. Their colleagues in ‘soft' science, however, have produced thus far a limited number of articles on this subject. This paper addresses this gap. Our main hypothesis is that existing Artificial Intelligence Large Language Models (LLMs) can closely align with human expert assessments in specialized social science surveys. To test this, we compare data from a multi-country expert survey with those collected from the two powerful LLMs created by OpenAI and Google. The statistical difference between the two sets of data is minimal in most cases, supporting our hypothesis, albeit with certain limitations and within specific parameters. The tested language models demonstrate domain-agnostic algorithmic accuracy, indicating an inherent ability to incorporate human knowledge and independently replicate human judgment across various subfields without specific training. We refer to this property as the ‘implicit intelligence' of Artificial Intelligence, representing a highly promising advancement for social science research.

Advancements in Text-to-Image Generation through Generative AI

Text-to-image generation, a fascinating intersection of natural language processing and computer vision, has witnessed remarkable progress in recent years. This research paper provides a comprehensive review of the state-of-the-art techniques, challenges, and applications in the field of text-to-image generation. The paper aims to analyze various approaches, discuss their strengths and limitations, and highlight potential directions for future research. Generative Artificial Intelligence (Generative AI) has revolutionized the fusion of textual information and visual content, giving rise to sophisticated Text-to-Image generators. In this context we will discuss dynamic landscape of Generative AI-driven text-to-image synthesis, exploring the state-of-the-art models, underlying architectures, and the impact of training strategies on the quality of generated images. The paper provides a comprehensive overview of Training strategies, encompassing dataset selection and fine-tuning approaches, are scrutinized for their impact on model performance. Common challenges, such as handling ambiguous textual descriptions and ensuring the avoidance of mode collapse, are addressed, offering insights into potential avenues for improvement. Training strategies, encompassing dataset selection and fine-tuning approaches, are scrutinized for their impact on model performance. Common challenges, such as handling ambiguous textual descriptions and ensuring the avoidance of mode collapse, are addressed, offering insights into potential avenues for improvement. Applications of Generative AI text-to-image generators are explored, ranging from content creation to virtual environment design, highlighting their versatility and real-world utility. Ethical considerations surrounding potential misuse, including the creation of deepfakes, are examined to foster a balanced understanding of the technology's societal implications. The research paper concludes with a forward-looking exploration of future directions in Generative AI text-to-image generation. Emphasizing the transformative potential of this technology, the paper envisions advancements in model architectures, training methodologies, and emerging applications, inviting researchers and practitioners to contribute to the ongoing evolution of this exciting field.

Exploring the Impact of Artificial Intelligence (AI-based) English Games in Enhancing English Communication Skills among Indonesian L2 Generation Z

The role of artificial intelligence (AI-based) English games in enhancing English communication skills among Indonesian Generation Z (Gen-Z) for whom English is a second language (L2), is the main focus of this study. Questionnaires to collect data consisted of both open and closed-ended questions. Seventy-eight Indonesian Gen- Z from different parts of Indonesia participated, and provides a strong representation of the country. This study is aimed to address a gap in the present literature by directly engaging with game players and examining their perspectives and experiences with actual games that they play for enjoyment rather than games designed for educational purposes. The study’s findings show that English AI-based games significantly help respondents develop their vocabulary, improve comprehension, and acquire confidence in English speaking. The findings also indicate that English AI-based game learning is favoured over traditional methods. The respondents noted gaming's immersive and interactive aspect as beneficial to language acquisition and skill development. These findings emphasise the transformative potential of English AI-based games in language learning and advocate for their integration into educational frameworks to better fulfil the needs of L2 learners, particularly Gen-Z.